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Found 27 papers, 15 papers with code
UniMASK: Unified Inference in Sequential Decision Problems
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
We demonstrate the utility of MoCapAct by using it to train a single hierarchical policy capable of tracking the entire MoCap dataset within dm_control and show the learned low-level component can be re-used to efficiently learn downstream high-level tasks.
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration.
Consistent Dropout for Policy Gradient Reinforcement Learning
Dropout has long been a staple of supervised learning, but is rarely used in reinforcement learning.
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
We present the design of a centralized benchmark for Reinforcement Learning which can help measure Sample Efficiency and Generalization in Reinforcement Learning by doing end to end evaluation of the training and rollout phases of thousands of user submitted code bases in a scalable way.
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents
Text-based games simulate worlds and interact with players using natural language.
BUTLER: Building Understanding in TextWorld via Language for Embodied Reasoning
ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.
Keep CALM and Explore: Language Models for Action Generation in Text-based Games
In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state.
How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
Text-based games are long puzzles or quests, characterized by a sequence of sparse and potentially deceptive rewards.
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language.
Working Memory Graphs
Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences.
Learning Calibratable Policies using Programmatic Style-Consistency
We study the problem of controllable generation of long-term sequential behaviors, where the goal is to calibrate to multiple behavior styles simultaneously.
Interactive Fiction Games: A Colossal Adventure
A hallmark of human intelligence is the ability to understand and communicate with language.
Multi-Preference Actor Critic
Policy gradient algorithms typically combine discounted future rewards with an estimated value function, to compute the direction and magnitude of parameter updates.
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection
We use the Convoluted Partitioning of Long Sequences (CPoLS) model, which processes Javascript files as byte sequences.
NAIL: A General Interactive Fiction Agent
Interactive Fiction (IF) games are complex textual decision making problems.
Counting to Explore and Generalize in Text-based Games
We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments.
TextWorld: A Learning Environment for Text-based Games
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games.
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Program synthesis is the task of automatically generating a program consistent with a specification.
Now I Remember! Episodic Memory For Reinforcement Learning
Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car.
Neural Program Meta-Induction
In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning.
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games.
Deep Reinforcement Learning in Parameterized Action Space
Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces.
Deep Recurrent Q-Learning for Partially Observable MDPs
Deep Reinforcement Learning has yielded proficient controllers for complex tasks.
Beyond Short Snippets: Deep Networks for Video Classification
Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval.
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